One of the most important aspects of evaluating a prospective wind farm is defining the wind resource for each wind turbine location that is proposed in an array of turbines that are distributed spatially across the wind farm site. Typically this is done by making measurements of the wind speed and direction at some number of locations within the project boundaries with towers or masts instrumented with anemometers and wind vanes, called a meteorological (met) tower. In some cases, there may be only one meteorological tower and in others, there may be several. Typically, more than one met tower is used to assess the wind resource of a potential commercial utility-scale wind farm. In some cases, sites may have as many as a dozen or more met towers. For sites with more than one met tower, it is common that one met tower is considered as a reference tower. The reference tower is typically the tower with the longest and most reliable wind speed and direction measurements. Wind speed and direction at other tower sites are correlated to measurements at the reference tower and are normalized to produce a consistent set of annual average wind speeds at relevant heights, usually the hub height of the proposed wind turbine or the upper measurement height of each meteorological tower. A joint frequency distribution of measured wind speed and wind direction, normalized for an average year, is used to define the wind climatology for a met tower location. Accurate measurement of wind speed and direction and calculation of wind climatology are known to one of ordinary skill in the art and further details are not provided here. Information about wind measurement and calculation of wind climatology can be found in Wind Characteristics, by Janardan Rohatgi and published by the Alternative Energy Institute, West Texas A&M University, 1994, incorporated herein by reference.
Typically, analysis of data collection from several meteorological towers across a site show that the wind does not blow with the same average annual wind speed from location to location. Many wind farms are developed in areas, such as the Great Plains, where there are few trees or other significant “surface roughness elements” that are well known to affect wind flow over the surface. In such places where the surface roughness is rather uniform, the variation in mean wind speed is driven to a large degree by variations in terrain elevation, or what are commonly referred to as “terrain effects”. Even where significant surface roughness elements exist, the spatial variation of annual average wind speed can often still be largely driven by terrain effects.
It has been observed that in areas where there are significant differences in terrain elevation across a wind farm site, the variance in mean wind speeds between met towers tends to be greater, indicating more extreme terrain effects. Conversely, areas with small differences in terrain elevation tend to have more subtle variations in wind speed, although spatial variation of annual average wind speed at these sites is still often significant and can have an important effect on predicted annual energy output for a wind farm at the proposed site.
Because it is not practical to measure the wind speed at each turbine site in a wind farm, it is necessary to extrapolate the observed wind speeds at the met tower sites to each turbine site and use this as a basis for calculating the energy output from the turbines that constitute the wind farm. Various wind flow models have been developed for this purpose and a number of such models are commercially available.
These models all use the wind speed and wind direction data measured at the met tower sites in conjunction with terrain elevation data, as in digital elevation models (DEM), to produce estimates of wind speeds at the proposed wind turbine locations. The models attempt to utilize variations in terrain elevation and surface roughness to calculate variations in wind speed and direction at specified turbine locations.
One of the most widely used models in the commercial wind energy industry is the Wind Atlas Analysis and Application Program (WAsP) developed by Risø DTU, Denmark. The WAsP model uses wind data from only one met tower at a time as input in order to estimate the wind speeds at the locations of each wind turbine site. This model makes calculations of the terrain elevations around the met tower location and the wind turbine locations and interprets the terrain calculations at the turbine sites with respect to the met tower to produce the wind speed estimates. The WAsP model is often referred to as a linear model and is reputed to be quite simple in its approach to the problem. Another similar model is MS-Micro/3, developed by Meteorological Service of Canada. Both of these models use similar physics in their calculations. Both have been found to result in large errors in prediction of annual average wind speed for some wind turbine locations, particularly at sites with complex terrain. Nevertheless, they continue to be utilized as the standard wind flow models in the wind energy industry. Newly developed wind flow models are often evaluated based on their ability to make more accurate predictions of spatial variation in wind climatology than WAsP.
Other, more complicated models are based on computational fluid dynamics (CFD), which solve fluid flow equations in consideration of the terrain, as represented by the DEM, in conjunction with the winds at the site, as represented by the meteorological tower data. Unlike WAsP and MS-Micro/3, many of these models can use wind information from more than one meteorological tower site at a time. Commercial CFD models include WindSim and Meteodyn.
Modeling the variation in wind climatology over a wind farm project site is one of the most challenging and difficult aspects of the wind resource assessment process. This challenge increases as terrain complexity increases. The differences in annual average wind speeds measured across a prospective wind farm site are in some cases baffling, defying a logical explanation, except to say that they are due to “terrain effects”. Indeed, in the absence of significant changes in surface roughness, wind speed variations across a site are due to the changes in terrain—ridges, valleys, and undulations that clearly must affect the wind speeds, but understanding how is not always clear. Prior art wind flow models often result in large errors in predicted wind speeds, particularly at sites with complex terrain.
Tests of the accuracy of prior art models has proven that their accuracy is not very good, particularly in conditions where the terrain is complex and the variability of the wind speeds are high. This lack of accuracy is detrimental to producing accurate estimates of energy output from wind farms.